1. Field of Application
The following description relates generally to telecommunications systems and wireless communications systems.
2. Prior Art
Wireless MIMO (multi-input, multi-output) system has been proven to be an effective way to improve the capacity or throughput of a wireless network. For example, an MIMO system with N transmit antennas and N receive antennas can have N-fold improvement over an SISO (single-input, single-output) system. MIMO schemes have been adopted in all major wireless cellular networks, such as 3G (3rd generation) and LTE (long-term evolution of 3G) systems.
While the benefits of MIMO to wireless networks can be huge, the actual performance of an MIMO system depends critically on the design of the MIMO demodulator. A narrowband MIMO system can be modeled as follows:r=Hx+u  (1)where x is an M×1 vector representing signals from M transmit antennas with E{xxH}=(ES/M)I, r is an N×1 vector representing received signals from N receive antennas, u is an N×1 vector representing independent, identically distributed (i.i.d.) noises with E{uuH}=N0I, and H is an N×M matrix representing the MIMO channel.
For the MIMO model in Eq. (1), optimum performance is achieved by maximum-likelihood (ML) demodulation, which seeks the most likely signal vector x given the received vector r. For high data rate applications, both signal size and number of antennas can be so large that ML demodulation becomes infeasible. Suboptimum algorithms for MIMO demodulations exist, such as spherical decoding, that achieves near-ML performance with reasonable complexity. Such suboptimum algorithms are referred to as “near-ML” algorithms or demodulators. On the other hand, a linear MIMO demodulator, such as MMSE (minimum mean square error) demodulator, has the least complexity. But the performance of a linear MIMO demodulator is often many dBs inferior to an ML or a near-ML demodulator.
The MIMO model in Eq. (1) is narrowband. The narrowband MIMO model is applicable to an LTE system that comprises a plurality of subcarriers each of which can be considered to be a narrowband signal. For a network that employs a wideband signal such as CDMA (code-division multiple access), the narrowband MIMO model does not directly apply. This is because the fading in a wideband wireless channel is not flat due to multipaths. The signal may be amplified in certain frequencies and may be in deep fades in some other frequencies. This non-flat fading is referred to as “frequency selective” fading. In time domain this is reflected as the inter-symbol interference (ISI) or inter-chip interference (ICI). Thus for a wideband signal, an equalizer, such as an MMSE equalizer, is typically applied to the signal to minimize the ISI or ICI pri- or to MIMO demodulation. The output of the equalizer is then MIMO-demodulated.
The output of the equalizer can still be modeled as in Eq. (1), but the noise vector u is no longer i.i.d. Instead the noise components in u can be highly correlated. Since the ML or near-ML algorithms are based on the i.i.d. noises, their direct applications to equalized wideband signal may lead to degraded performance, and sometimes the performance can even be worse than direct decision (slicing) on the equalizer output. As a result, a wideband MIMO receiver often employs a linear equalizer to suppress the ISI or ICI, followed by a decision or slicing function to recover the transmitted signal vector x. Since no ML demodulation is used, the performance can be far from optimal. Moreover, using a slicing function after equalization assumes the equivalent MIMO channel H is diagonal, which is generally not true, thus further degrading the performance.
Thus there can be significant potentials for improving the performance of a wideband MIMO receiver, and a strong need exists for a method, system, and apparatus that overcome aforementioned shortcomings.